{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples and Exercises from Think Stats, 2nd Edition\n",
    "\n",
    "http://thinkstats2.com\n",
    "\n",
    "Copyright 2016 Allen B. Downey\n",
    "\n",
    "MIT License: https://opensource.org/licenses/MIT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import brfss\n",
    "\n",
    "import thinkstats2\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plots\n",
    "\n",
    "I'll start with the data from the BRFSS again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = brfss.ReadBrfss(nrows=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function selects a random subset of a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SampleRows(df, nrows, replace=False):\n",
    "    indices = np.random.choice(df.index, nrows, replace=replace)\n",
    "    sample = df.loc[indices]\n",
    "    return sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll extract the height in cm and the weight in kg of the respondents in the sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = SampleRows(df, 5000)\n",
    "heights, weights = sample.htm3, sample.wtkg2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a simple scatter plot with `alpha=1`, so each data point is fully saturated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Scatter(heights, weights, alpha=1)\n",
    "thinkplot.Config(xlabel='Height (cm)',\n",
    "                 ylabel='Weight (kg)',\n",
    "                 axis=[140, 210, 20, 200],\n",
    "                 legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data fall in obvious columns because they were rounded off.  We can reduce this visual artifact by adding some random noice to the data.\n",
    "\n",
    "NOTE: The version of `Jitter` in the book uses noise with a uniform distribution.  Here I am using a normal distribution.  The normal distribution does a better job of blurring artifacts, but the uniform distribution might be more true to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Jitter(values, jitter=0.5):\n",
    "    n = len(values)\n",
    "    return np.random.normal(0, jitter, n) + values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heights were probably rounded off to the nearest inch, which is 2.8 cm, so I'll add random values from -1.4 to 1.4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "heights = Jitter(heights, 1.4)\n",
    "weights = Jitter(weights, 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's what the jittered data look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Scatter(heights, weights, alpha=1.0)\n",
    "thinkplot.Config(xlabel='Height (cm)',\n",
    "                 ylabel='Weight (kg)',\n",
    "                 axis=[140, 210, 20, 200],\n",
    "                 legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns are gone, but now we have a different problem: saturation.  Where there are many overlapping points, the plot is not as dark as it should be, which means that the outliers are darker than they should be, which gives the impression that the data are more scattered than they actually are.\n",
    "\n",
    "This is a surprisingly common problem, even in papers published in peer-reviewed journals.\n",
    "\n",
    "We can usually solve the saturation problem by adjusting `alpha` and the size of the markers, `s`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Scatter(heights, weights, alpha=0.1, s=10)\n",
    "thinkplot.Config(xlabel='Height (cm)',\n",
    "                 ylabel='Weight (kg)',\n",
    "                 axis=[140, 210, 20, 200],\n",
    "                 legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's better.  This version of the figure shows the location and shape of the distribution most accurately.  There are still some apparent columns and rows where, most likely, people reported their height and weight using rounded values.  If that effect is important, this figure makes it apparent; if it is not important, we could use more aggressive jittering to minimize it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative to a scatter plot is something like a `HexBin` plot, which breaks the plane into bins, counts the number of respondents in each bin, and colors each bin in proportion to its count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.HexBin(heights, weights)\n",
    "thinkplot.Config(xlabel='Height (cm)',\n",
    "                 ylabel='Weight (kg)',\n",
    "                 axis=[140, 210, 20, 200],\n",
    "                 legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case the binned plot does a pretty good job of showing the location and shape of the distribution.  It obscures the row and column effects, which may or may not be a good thing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:**  So far we have been working with a subset of only 5000 respondents.  When we include the entire dataset, making an effective scatterplot can be tricky.  As an exercise, experiment with `Scatter` and `HexBin` to make a plot that represents the entire dataset well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting percentiles\n",
    "\n",
    "Sometimes a better way to get a sense of the relationship between variables is to divide the dataset into groups using one variable, and then plot percentiles of the other variable.\n",
    "\n",
    "First I'll drop any rows that are missing height or weight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned = df.dropna(subset=['htm3', 'wtkg2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then I'll divide the dataset into groups by height."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.arange(135, 210, 5)\n",
    "indices = np.digitize(cleaned.htm3, bins)\n",
    "groups = cleaned.groupby(indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the number of respondents in each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 305\n",
      "1 228\n",
      "2 477\n",
      "3 2162\n",
      "4 18759\n",
      "5 45761\n",
      "6 70610\n",
      "7 72138\n",
      "8 61725\n",
      "9 49938\n",
      "10 43555\n",
      "11 20077\n",
      "12 7784\n",
      "13 1777\n",
      "14 405\n",
      "15 131\n"
     ]
    }
   ],
   "source": [
    "for i, group in groups:\n",
    "    print(i, len(group))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute the CDF of weight within each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_heights = [group.htm3.mean() for i, group in groups]\n",
    "cdfs = [thinkstats2.Cdf(group.wtkg2) for i, group in groups]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then extract the 25th, 50th, and 75th percentile from each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for percent in [75, 50, 25]:\n",
    "    weight_percentiles = [cdf.Percentile(percent) for cdf in cdfs]\n",
    "    label = '%dth' % percent\n",
    "    thinkplot.Plot(mean_heights, weight_percentiles, label=label)\n",
    "    \n",
    "thinkplot.Config(xlabel='Height (cm)',\n",
    "                 ylabel='Weight (kg)',\n",
    "                 axis=[140, 210, 20, 200],\n",
    "                 legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Yet another option is to divide the dataset into groups and then plot the CDF for each group.  As an exercise, divide the dataset into a smaller number of groups and plot the CDF for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function computes the covariance of two variables using NumPy's `dot` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Cov(xs, ys, meanx=None, meany=None):\n",
    "    xs = np.asarray(xs)\n",
    "    ys = np.asarray(ys)\n",
    "\n",
    "    if meanx is None:\n",
    "        meanx = np.mean(xs)\n",
    "    if meany is None:\n",
    "        meany = np.mean(ys)\n",
    "\n",
    "    cov = np.dot(xs-meanx, ys-meany) / len(xs)\n",
    "    return cov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103.33290857697797"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heights, weights = cleaned.htm3, cleaned.wtkg2\n",
    "Cov(heights, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Covariance is useful for some calculations, but it doesn't mean much by itself.  The coefficient of correlation is a standardized version of covariance that is easier to interpret."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Corr(xs, ys):\n",
    "    xs = np.asarray(xs)\n",
    "    ys = np.asarray(ys)\n",
    "\n",
    "    meanx, varx = thinkstats2.MeanVar(xs)\n",
    "    meany, vary = thinkstats2.MeanVar(ys)\n",
    "\n",
    "    corr = Cov(xs, ys, meanx, meany) / np.sqrt(varx * vary)\n",
    "    return corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation of height and weight is about 0.51, which is a moderately strong correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5087364789734771"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Corr(heights, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy provides a function that computes correlations, too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.50873648],\n",
       "       [0.50873648, 1.        ]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(heights, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is a matrix with self-correlations on the diagonal (which are always 1), and cross-correlations on the off-diagonals (which are always symmetric)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pearson's correlation is not robust in the presence of outliers, and it tends to underestimate the strength of non-linear relationships.\n",
    "\n",
    "Spearman's correlation is more robust, and it can handle non-linear relationships as long as they are monotonic.  Here's a function that computes Spearman's correlation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def SpearmanCorr(xs, ys):\n",
    "    xranks = pd.Series(xs).rank()\n",
    "    yranks = pd.Series(ys).rank()\n",
    "    return Corr(xranks, yranks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For heights and weights, Spearman's correlation is a little higher:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5405846262320476"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SpearmanCorr(heights, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Pandas `Series` provides a method that computes correlations, and it offers `spearman` as one of the options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SpearmanCorr(xs, ys):\n",
    "    xs = pd.Series(xs)\n",
    "    ys = pd.Series(ys)\n",
    "    return xs.corr(ys, method='spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result is the same as for the one we wrote."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5405846262320457"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SpearmanCorr(heights, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative to Spearman's correlation is to transform one or both of the variables in a way that makes the relationship closer to linear, and the compute Pearson's correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5317282605983465"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Corr(cleaned.htm3, np.log(cleaned.wtkg2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using data from the NSFG, make a scatter plot of birth weight versus mother’s age. Plot percentiles of birth weight versus mother’s age. Compute Pearson’s and Spearman’s correlations. How would you characterize the relationship between these variables?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import first\n",
    "\n",
    "live, firsts, others = first.MakeFrames()\n",
    "live = live.dropna(subset=['agepreg', 'totalwgt_lb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution goes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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